Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities.
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ABSTRACT:
SUBMITTER: Cao R
PROVIDER: S-EPMC8333027 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Evidence on the health effects of extreme temperatures and air pollution is copious. However few studies focused on their interaction. The aim of this study is to evaluate daily PM10 and ozone as potential effect modifiers of the relationship between temperature and natural mortality in 25 Italian cities. Time-series analysis was run for each city. To evaluate interaction, a tensor product between mean air temperature (lag 0?3) and either PM10 or ozone (both lag 0?5) was defined and temperature estimates were extrapolated at low, medium, and high levels of pollutants. Heat effects were estimated as percent change in mortality for increases in temperature between 75th and 99th percentiles. Results were pooled by geographical area. Differential temperature-mortality risks by air pollutants were found. For PM10, estimates ranged from 3.9% (low PM10) to 14.1% (high PM10) in the North, from 3.6% to 24.4% in the Center, and from 7.5% to 21.6% in the South. Temperature-related mortality was similarly modified by ozone in northern and central Italy, while no effect modification was observed in the South. This study underlines the synergistic effects of heat and air pollution on mortality. Considering the predicted increase in heat waves and stagnation events in the Mediterranean countries such as Italy, it is time to enclose air pollution within public health heat prevention plans.
| S-EPMC6122066 | biostudies-literature
Project description:PurposeThe association between air pollutant (PM2.5, PM10, NO2, and O3) concentrations and daily number of COVID-19 confirmed cases and related deaths were evaluated in three major Iranian cities (Tehran, Mashhad, and Tabriz).MethodsHourly concentrations of air pollutants and daily number of PCR-confirmed cases and deaths of COVID-19 were acquired (February 20th, 2020 to January 4th, 2021). A generalized additive model (GAM) assuming a quasi-Poisson distribution was used to model the associations in each city up to lag-day 7 (for mortality) and 14 (for morbidity). Then, the city-specific estimates were meta-analyzed using a fixed effect model to obtain the overall relative risks (RRs).ResultsA total of 114,964 confirmed cases and 21,549 deaths were recorded in these cities. For confirmed cases, exposure to PM2.5, NO2, and O3 for several lag-days showed significant associations. In case of mortality, meta-analysis estimated that the RRs for PM2.5, PM10, NO2, and O3 concentrations were 1.06 (95% CI: 0.99, 1.13), 1.06 (95% CI: 0.93, 1.19), 1.15 (95% CI: 0.93, 1.38), and 1.07 (95% CI: 0.84, 1.31), respectively. Despite several positive associations with all air pollutants over multiple lag-days, COVID-19 mortality was only significantly associated with NO2 on lag-days 0-1 and 1 with the RRs of 1.35 (95% CI: 1.04, 1.67) and 1.16 (95% CI: 1.02, 1.31), respectively.ConclusionThis study showed that air pollution can be a factor exacerbating COVID-19 infection and clinical outcomes. Actions should be taken to reduce the exposure of the public and particularly patients to ambient air pollutants.Supplementary informationThe online version contains supplementary material available at 10.1007/s40201-021-00736-4.
| S-EPMC8553398 | biostudies-literature
Project description:RationaleCertain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose-response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants.ObjectivesInvestigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma.MethodsDaily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993-2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis.Measurements and main resultsBoth ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations.ConclusionsEven at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.
| S-EPMC2921597 | biostudies-literature
Project description:BACKGROUND:Coarse particulate matter with aerodynamic diameter between 2.5 and [Formula: see text] ([Formula: see text]) air pollution is a severe environmental problem in developing countries, but its challenges to public health were rarely evaluated. OBJECTIVE:We aimed to investigate the associations between day-to-day changes in [Formula: see text] and cause-specific mortality in China. METHODS:We conducted a nationwide daily time-series analysis in 272 main Chinese cities from 2013 to 2015. The associations between [Formula: see text] concentrations and mortality were analyzed in each city using overdispersed generalized additive models. Two-stage Bayesian hierarchical models were used to estimate national and regional average associations, and random-effect models were used to pool city-specific concentration-response curves. Two-pollutant models were adjusted for fine particles with aerodynamic diameter [Formula: see text] ([Formula: see text]) or gaseous pollutants. RESULTS:Overall, we observed positive and approximately linear concentration-response associations between [Formula: see text] and daily mortality. A [Formula: see text] increase in [Formula: see text] was associated with higher mortality due to nonaccidental causes [0.23%; 95% posterior interval (PI): 0.13, 0.33], cardiovascular diseases (CVDs; 0.25%; 95% PI: 0.13, 0.37), coronary heart disease (CHD; 0.21%; 95% PI: 0.05, 0.36), stroke (0.21%; 95% PI: 0.08, 0.35), respiratory diseases (0.26%; 95% PI: 0.07, 0.46), and chronic obstructive pulmonary disease (COPD; 0.34%; 95% PI: 0.12, 0.57). Associations were stronger for cities in southern vs. northern China, with significant differences for total and cardiovascular mortality. Associations with [Formula: see text] were of similar magnitude to those for [Formula: see text] in both single- and two-pollutant models with mutual adjustment. Associations were robust to adjustment for gaseous pollutants other than nitrogen dioxide and sulfur dioxide. Meta-regression indicated that a larger positive correlation between [Formula: see text] and [Formula: see text] predicted stronger city-specific associations between [Formula: see text] and total mortality. CONCLUSIONS:This analysis showed significant associations between short-term [Formula: see text] exposure and daily nonaccidental and cardiopulmonary mortality based on data from 272 cities located throughout China. Associations appeared to be independent of exposure to [Formula: see text], carbon monoxide, and ozone. https://doi.org/10.1289/EHP2711.
| S-EPMC6378682 | biostudies-literature
Project description:Limited evidence is available regarding the association between acute exposure to ambient air pollutants and the risk of urticaria, even though the skin is an organ with direct contact with the external environment. This study utilized generalized additive models to investigate the association between particulate matter with an aerodynamic diameter smaller than 10 μm (PM10) and 2.5 μm (PM2.5), nitrogen dioxide (NO2) and sulfur dioxide (SO2), and daily outpatient visits for urticaria in Guangzhou, China from 2013 to 2017. We also estimated the attributable fraction of urticaria outpatient visits due to air pollution. A total of 216,648 outpatient visits due to urticaria occurred during the study period. All air pollutants were significantly associated with an increased excess risk of urticaria. Each 10 μg/m3 increase in PM2.5, PM10, NO2, and SO2 was associated with an increase of 1.23% (95% CI: 0.42%, 2.06%), 0.88% (95% CI: 0.28%, 1.49%), 3.09% (95% CI: 2.16%, 4.03%), and 2.82% (95% CI: 0.93%, 4.74%) in hospital visits for urticaria at lag05, respectively. It was estimated that 3.77% (95% CI: 1.26%, 6.38%), 1.91% (95% CI: 0.60%, 3.26%), 6.36% (95% CI: 4.38%, 8.41%), and 0.08% (95% CI: 0.03%, 0.14%) of urticaria outpatient visits were attributable to PM2.5, PM10, NO2, and SO2 using the World Health Organization's air quality guideline as the reference. Relatively stronger associations were observed during the cold season. This study indicates that short-term air pollution may play a significant role in outpatient visits for urticaria, and that such relationships could be modified by season.
| S-EPMC10747676 | biostudies-literature
Project description:With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.
| S-EPMC11297238 | biostudies-literature
Project description:Air pollution has happened to be one of the mounting alarms to be concerned with in many Indian cities. COVID-19 epidemic endow with a unique opportunity to report the degree of air quality improvement due to the nationwide lockdown in 10 most polluted cities across the country. National Air Quality Index (NAQI) based on continuous monitoring records of seven criteria pollutants (i.e. common air pollutants with known health impacts e.g. PM10, PM2.5, CO, NO2, SO2, NH3 and O3) for a total of 59 stations across the cities, satellite image derived Aerosol Optical Depth (AOD) and few statistical tools are employed to derive the outcomes. NAQI results convey that 8 cities out of the 10 air quality restored to good to satisfactory category during the lockdown period. Within week+1 of the lockdown period, PM10 and PM2.5 concentrations have suppressed below the permissible limit in all cities. CO and NO2 have reduced to about -30% and -57% respectively during the lockdown period. Diurnal concentrations of PM10 and PM2.5 have dropped drastically on the very 4th day of lockdown and become consistent with minor hourly vacillation. In April 2020 the AOD amount was reduced to about 36% and 18% in contrast to April 2018 and April 2019 respectively. This add-on reporting of the possible recovery extent in air quality may help to guide alternative policy intervention in form of short term lockdown so as to testify whether this type of unconventional policy decisions may be put forward to attain a green environment. Because, despite numerous restoration plans, air pollution levels have risen unabated in these cities. However, detailed inventory needs to be focused on identifying the localized pollution hotspots (i.e. source contribution).
| S-EPMC7330599 | biostudies-literature
Project description:ObjectiveTo evaluate the short term associations between nitrogen dioxide (NO2) and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide, using a uniform analytical protocol.DesignTwo stage, time series approach, with overdispersed generalised linear models and multilevel meta-analysis.Setting398 cities in 22 low to high income countries/regions.Main outcome measuresDaily deaths from total (62.8 million), cardiovascular (19.7 million), and respiratory (5.5 million) causes between 1973 and 2018.ResultsOn average, a 10 μg/m3 increase in NO2 concentration on lag 1 day (previous day) was associated with 0.46% (95% confidence interval 0.36% to 0.57%), 0.37% (0.22% to 0.51%), and 0.47% (0.21% to 0.72%) increases in total, cardiovascular, and respiratory mortality, respectively. These associations remained robust after adjusting for co-pollutants (particulate matter with aerodynamic diameter ≤10 μm or ≤2.5 μm (PM10 and PM2.5, respectively), ozone, sulfur dioxide, and carbon monoxide). The pooled concentration-response curves for all three causes were almost linear without discernible thresholds. The proportion of deaths attributable to NO2 concentration above the counterfactual zero level was 1.23% (95% confidence interval 0.96% to 1.51%) across the 398 cities.ConclusionsThis multilocation study provides key evidence on the independent and linear associations between short term exposure to NO2 and increased risk of total, cardiovascular, and respiratory mortality, suggesting that health benefits would be achieved by tightening the guidelines and regulatory limits of NO2.
| S-EPMC7988454 | biostudies-literature
Project description:ObjectiveTo investigate the association between ambient air pollution and stroke morbidity in different subgroups and seasons.MethodsWe performed a time-series analysis based on generalised linear models to study the short-term exposure-response relationships between air pollution and stroke hospitalisations, and conducted subgroup analyses to identify possible sensitive populations.ResultsFor every 10 µg/m3 increase in the concentration of air pollutants, across lag 0-3 days, the relative risk of stroke hospitalisation was 1.029 (95% CI 1.013 to 1.045) for PM2.5, 1.054 (95% CI 1.031 to 1.077) for NO2 and 1.012 (95% CI 1.002 to 1.022) for O3. Subgroup analyses showed that statistically significant associations were found in both men and women, middle-aged and older populations, and both cerebral infarction and intracerebral haemorrhage. The seasonal analyses showed that statistically significant associations were found only in the winter.ConclusionsOur study indicates that short-term exposure to PM2.5, NO2 and O3 may induce stroke morbidity, and the government should take actions to mitigate air pollution and protect sensitive populations.
| S-EPMC7103818 | biostudies-literature
Project description:The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM2.5 (20%), PM10(24%), SO2 (12%), and O3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM2.5 (3%), PM10 (1%), SO2 (7.7%), and O3 (10%). Similarly, there is an increment in COVID-19 deaths for PM2.5 (2.8%), PM10 (1%), SO2 (4.5%), and O3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-023-00512-6.
| S-EPMC10010960 | biostudies-literature